Material

ROAD is the first benchmark of its kind, designed to allow the autonomous vehicles community to investigate the use of semantically meaningful representations of dynamic road scenes to facilitate situation awareness and decision making for autonomoous driving.

ROAD is a multilabel dataset containing 22 long-duration videos (ca 8 minutes each) comprising 122K frames annotated in terms of *road events*, defined as triplets E = (Agent, Action, Location) and represented as a series of frame-wise bounding box detections.

ROAD has the ambition to become the reference benchmark for agent and event detection, intention and trajectory prediction, future events anticipation, modelling of complex road activities, instance- and class-incremental continual learning, machine theory of mind and automated decision making.

ROAD-Waymo is an extensive dataset for the development and benchmarking of techniques for agent, action, location and event detection in road scenes, provided as a layer upon the (US) Waymo Open dataset. Considerably larger and more challenging than any existing dataset of this nature, it features multiple cities, 198k annotated video frames, 54k agent tubes, 3.9M bounding boxes and a total of 12.4M labels. The integrity of the dataset has been confirmed and enhanced via a novel annotation pipeline designed for automatically identifying violations of requirements specifically designed for this dataset.

ROAD-R is the first publicly available dataset for autonomous driving with requirements expressed as logical constraints. It extends the ROAD dataset and the requirements are propositional logic constraints provided in conjunctive normal form (CNF) with clauses and express background knowledge applicable in autonomous driving scenarios.